# LTX Video-0.9.7 13B Distilled API

> تبدیل متن به ویدئو با مدل قدیمی‌تر 0.9.7 distilled 13B، با پشتیبانی از LoRA و تعداد فریم زیاد

با مدل تقطیرشدهٔ 0.9.7 و با استفاده از `prompt` اجباری ویدیو تولید می‌کند. وضوح 480p یا 720p، نسبت تصویر 9:16، 1:1 یا 16:9 و `num_frames` از 9 تا 1441 با 1 تا 60 fps است. هر یک از دو گذر نمونه‌برداری 2 تا 12 گام دارد. `loras` اختیاری، گذر جزئیات اضافی و گسترش پرامپت در دسترس‌اند.

- Model id: `ltx-video-13b-distilled`
- Made by: Lightricks
- Category: Text to Video
- Output: video
- Price per second of your output video: from $0.04 / second of output video (cheapest run $0.04 (40 API credits))
- Run: `POST https://api.doitong.com/v1/run/ltx-video-13b-distilled`
- Quote: `POST https://api.doitong.com/v1/quote/ltx-video-13b-distilled`
- Docs page: https://doitong.com/fa/developers/models/ltx-video-13b-distilled

## Pricing

Price: **from $0.04 / second of output video** (the lowest tier; the quote prices your exact input). Price per second of your output video: the input files are measured before the run (length, resolution, frame rate). The quote measures the same files and is exactly what the run charges.

The full example input below costs $0.202 = 202 API credits (5.0417 s output video × $0.04).

Prices are the upstream price in USD, paid from the API balance in API credits (1000 API credits = 1 USD). `POST https://api.doitong.com/v1/quote/ltx-video-13b-distilled` with the same JSON body returns the exact price for that input; the quote is exactly what a run charges. Invalid input is never charged and failed or canceled requests are refunded automatically.

## Input parameters

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `prompt` |  | string | yes |  |  | دستورهای متنی هدایت‌کننده تولید. |
| `negative_prompt` |  | string | no | `worst quality, inconsistent motion, blurry, jittery, distorted` |  | دستورهای متنی منفی برای تولید. |
| `loras` |  | array of object {path, weight_name, scale} | no | `[]` |  | وزن‌های LoRA اعمال‌شده هنگام تولید. |
| `loras[].path` |  | string | yes |  |  | مکان وزن‌های LoRA، به‌صورت URL یا مسیر. |
| `loras[].weight_name` |  | string \| null | no |  |  | نام وزن LoRA برای انتخاب میان چند وزن در مخزن HuggingFace؛ فقط مخزن‌هایی که چند وزن دارند به آن نیاز دارند. این تنظیم مگر آن‌که `path` یک مخزن HuggingFace را مشخص کند، اثری ندارد. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | ضریب مقیاس‌دهنده وزن LoRA هنگام بارگذاری. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `720p` | وضوح ویدیوی خروجی. |
| `aspect_ratio` |  | enum (string) | no | `16:9` | `9:16`, `1:1`, `16:9` | نسبت تصویر ویدیوی نهایی. |
| `seed` |  | integer \| null | no |  |  | seed کنترل‌کننده تصادفی‌بودن هنگام تولید. |
| `num_frames` |  | integer | no | `121` | 9 – 1,441 | تعداد کل فریم‌های ویدیو. |
| `first_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | تعداد گام‌های استنتاج برای گذر اول. |
| `second_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | تعداد گام‌های استنتاج برای گذر دوم. |
| `second_pass_skip_initial_steps` |  | integer | no | `5` | 1 – 11 | تعداد اولیه گام‌های استنتاجی که در گذر دوم حذف می‌شوند و آن گذر را به‌جای تغییرات بزرگ‌مقیاس به سمت جزئیات ظریف سوق می‌دهند. |
| `frame_rate` |  | integer | no | `24` | 1 – 60 | نرخ فریم ویدیو. |
| `expand_prompt` |  | boolean | no | `false` |  | گسترش پرامپت از طریق مدل زبانی. |
| `reverse_video` |  | boolean | no | `false` |  | معکوس‌کردن ترتیب پخش ویدیو. |
| `enable_safety_checker` |  | boolean | no | `true` |  | کلید بررسی ایمنی که حالت خاموش آن فقط برای حساب‌های مجاز پذیرفته می‌شود. همه درخواست‌های فاقد این مجوز بررسی ایمنی می‌شوند. |
| `enable_detail_pass` |  | boolean | no | `false` |  | گذر پالایش دوم اختیاری که در صورت فعال‌بودن جزئیات ویدیو را بهبود می‌دهد. |
| `temporal_adain_factor` |  | number | no | `0.5` | 0 – 1 | شدت نرمال‌سازی تطبیقی نمونه (AdaIN) که در مقادیر بالاتر انسجام رنگ و در مقادیر پایین‌تر تنوع رنگ بیشتری ایجاد می‌کند. این فرایند روی قطعه‌های تولیدشده به‌جز قطعه اول اعمال می‌شود و توزیع رنگ را در سراسر ویدیو نرمال می‌کند تا افزایش تدریجی اشباع یا کنتراست را کاهش دهد. |
| `tone_map_compression_ratio` |  | number | no | `0` | 0 – 1 | سطح فشرده‌سازی نگاشت تُن، از بدون فشرده‌سازی در 0.0 تا بیشترین فشرده‌سازی در 1.0. فشرده‌سازی دامنه پویای ویدیو به این روش کیفیت بصری را بهبود می‌دهد. |

## Example input

Required parameters only:

```json
{
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

A fuller example:

```json
{
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

## Run it

Set `DOITONG_API_KEY` to a key from https://doitong.com/developers/dashboard#keys. `?wait=20` returns the finished request in the same call when it is ready within 20 seconds; otherwise poll `GET /requests/{id}` (it also accepts `?wait=`), or pass `webhook_url`.

### curl

```bash
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/ltx-video-13b-distilled?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  }'

# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.com/v1";
const headers = {
  "Authorization": "Bearer " + process.env.DOITONG_API_KEY,
  "Content-Type": "application/json",
};

// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx-video-13b-distilled?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  }),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);

// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
  res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
  request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
```

### Python

```python
import os
import requests

API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}

payload = {
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ltx-video-13b-distilled", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
    raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])

# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
    request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
    raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
```

### MCP

The MCP server at `https://api.doitong.com/v1/mcp` exposes the tools `search_models`, `get_model`, `quote`, `run`, `get_request`, `cancel_request`, `list_requests`, `create_upload` and `get_account`. A `run` call for this model:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "run",
    "arguments": {
      "model_id": "ltx-video-13b-distilled",
      "input": {
        "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
      },
      "wait_seconds": 20
    }
  }
}
```

## Response

Every call returns a request object. `status` is one of `queued`, `running`, `succeeded`, `failed`, `canceled`. `outputs` lists the generated files with URLs on our storage (kept forever, or 7 days with `?storage=temporary`; `storage` says which); `output` is the model's own result object with the same URLs.

```json
{
  "id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
  "object": "request",
  "model": "ltx-video-13b-distilled",
  "status": "succeeded",
  "created_at": "2026-10-20T12:00:00Z",
  "started_at": "2026-10-20T12:00:02Z",
  "completed_at": "2026-10-20T12:00:09Z",
  "queue_position": null,
  "cost": {
    "credits": 202,
    "usd": "0.202",
    "charged": 202,
    "refunded": 0
  },
  "input": {
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  },
  "output": {
    "video": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
      "content_type": "video/mp4",
      "file_name": "0-output.mp4",
      "file_size": 1843200
    },
    "prompt": "A cinematic fast-tracking shot follows a vintage, teal camper van as it descends a winding mountain trail. The van, slightly weathered but well-maintained, is the central focus, its retro design emphasized by the motion blur. Medium shot reveals the dusty, ochre trail, edged with vibrant green pine trees. Close-up on the van's tires shows the gravel spraying, highlighting the speed and rugged terrain. Sunlight filters through the trees, casting dappled shadows on the van and the trail. The background is a hazy, majestic mountain range bathed in warm, golden light. The overall mood is adventurous and exhilarating. High resolution 4k movie scene.",
    "seed": 42
  },
  "outputs": [
    {
      "type": "video",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
      "content_type": "video/mp4",
      "file_size": 1843200,
      "width": 1280,
      "height": 720,
      "duration": 5,
      "stored": true
    }
  ],
  "error": null,
  "metrics": {
    "inference_seconds": 6.8
  },
  "urls": {
    "self": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `video` | object {url, content_type, file_name, file_size} | فایل ویدیویی تولیدشده. |
| `prompt` | string | پرامپتی که برای تولید خروجی استفاده شده است. |
| `seed` | integer | مقدار Seed اعمال‌شده هنگام تولید. |

## Related models

- [LTX Video-0.9.7 13B Distilled · Image to Video](https://doitong.com/developers/models/ltx-video-13b-distilled/image-to-video.md) (`ltx-video-13b-distilled/image-to-video`): from $0.04 / second of output video
- [LTX Video-0.9.7 13B Distilled · Extend](https://doitong.com/developers/models/ltx-video-13b-distilled/extend.md) (`ltx-video-13b-distilled/extend`): $0.04 / video
- [LTX Video-0.9.7 13B Distilled · Multiconditioning](https://doitong.com/developers/models/ltx-video-13b-distilled/multiconditioning.md) (`ltx-video-13b-distilled/multiconditioning`): from $0.04 / second of output video
- [LTX 2.5 Pro Text to Video](https://doitong.com/developers/models/lightricks/ltx-2.5/text-to-video/pro.md) (`lightricks/ltx-2.5/text-to-video/pro`): from $0.12 / second
- [FLUX 3 Max Text to Video](https://doitong.com/developers/models/blackforestlabs/flux-3-max/text-to-video.md) (`blackforestlabs/flux-3-max/text-to-video`): from $0.0425 / second of output video
- [Grok Imagine Video 1.5 Text to Video](https://doitong.com/developers/models/xai/grok-imagine-video/v1.5/text-to-video.md) (`xai/grok-imagine-video/v1.5/text-to-video`): from $0.08 / second
- [Kling 3.0 Pro Text to Video](https://doitong.com/developers/models/kling-video/v3/pro/text-to-video.md) (`kling-video/v3/pro/text-to-video`): from $0.112 / second
- [MiniMax H3 Text to Video](https://doitong.com/developers/models/minimax/h3/text-to-video.md) (`minimax/h3/text-to-video`): from $0.05 / second

## Links

- HTML page: https://doitong.com/developers/models/ltx-video-13b-distilled
- All models and guides for AI agents: https://doitong.com/developers/llms.txt
- OpenAPI 3.1 with this model's typed request body: https://doitong.com/developers/openapi.json?model=ltx-video-13b-distilled
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/ltx-video-13b-distilled